Manifold Blog

Introduction

The Python ecosystem offers a number of incredibly useful open source tools for data scientists and machine learning (ML) practitioners. One such tool is Dask, available from Anaconda. At Manifold, we have used Dask extensively to build scalable ML pipelines.

Before I started at Manifold, I knew a little about the machine learning (ML) space, but wanted a better grounding in it. I asked CEO Vinay Seth Mohta for some more information, and found the resources he shared tremendously helpful. My research turned up some additional resources of my own, as well.

The following resource compilation includes those items, as well as a few added by others on the Manifold team. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that others who embark on this journey can have an even smoother and more fulfilling experience.

Introduction

The last five years have seen many new developments in reinforcement learning (RL), a very interesting sub-field of machine learning (ML). Publication of "Deep Q-Networks" from DeepMind, in particular, ushered in a new era. As RL comes into its own, it's becoming clear that a key concept in all RL algorithms is the tradeoff between exploration and exploitation. In this post, we will simulate a problem called the "multi-armed bandit" in order to understand the details of this tradeoff.

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We publish occasional blog posts about our client work, open source projects, and conference experiences. We focus on industry insights and practical takeaways to help you accelerate your data roadmap and create business value.